Journal ArticleDOI
Local versus Global Lessons for Defect Prediction and Effort Estimation
Tim Menzies,A. Butcher,David R. Cok,Andrian Marcus,Lucas Layman,Forrest Shull,Burak Turhan,Thomas Zimmermann +7 more
TLDR
It is concluded that when researchers attempt to draw lessons from some historical data source, they should ignore any existing local divisions into multiple sources, cluster across all available data, then restrict the learning of lessons to the clusters from other sources that are nearest to the test data.Abstract:
Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local versus global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect datasets from the PROMISE repository. Rule learners generated lessons learned from all the data, from local projects, or just from each cluster. The results indicate that the lessons learned after combining small parts of different data sources (i.e., the clusters) were superior to either generalizations formed over all the data or local lessons formed from particular projects. We conclude that when researchers attempt to draw lessons from some historical data source, they should 1) ignore any existing local divisions into multiple sources, 2) cluster across all available data, then 3) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.read more
Citations
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Software engineering economics
TL;DR: In this article, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
Journal ArticleDOI
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
TL;DR: CPDP is still a challenge and requires more research before trustworthy applications can take place and this work synthesises literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances.
Journal ArticleDOI
Tuning for software analytics
Wei Fu,Tim Menzies,Xipeng Shen +2 more
TL;DR: This paper finds that it is no longer enough to just run a data miner and present the result without conducting a tuning optimization study, and that standard methods in software analytics need to change.
Journal ArticleDOI
Studying just-in-time defect prediction using cross-project models
Yasutaka Kamei,Takafumi Fukushima,Shane McIntosh,Kazuhiro Yamashita,Naoyasu Ubayashi,Ahmed E. Hassan +5 more
TL;DR: An empirical study on 11 open source projects finds that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that select models trained using other projects that are similar to the testing project, and combine the models of several other projects to produce an ensemble model.
Journal ArticleDOI
A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches
TL;DR: A benchmark for CPDP is provided and it is determined that an approach proposed by Camargo Cruz and Ochimizu (2009) based on data standardization performs best and is always ranked among the statistically significant best results for all metrics and data sets.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Programs for Machine Learning
Steven L. Salzberg,Alberto Segre +1 more
TL;DR: In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
Book
Tabu Search
Fred Glover,Manuel Laguna +1 more
TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.
Software engineering economics
TL;DR: In this paper, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.